【摘要】 目的提出一种基于置换检验(permutation test)和主成分分析(PCA)的检验方法-permutation-PCA。探讨在病例对照关联分析中permutation-PCA在不同遗传模型下的表现。方法基于"首吸飘感"对照组数据分别模拟产生7种不同遗传模型下的病例对照基因分型数据,采用permutation-PCA方法对模拟数据进行检验,并进一步用permuta-tion-PCA方法检验"首吸飘感"基因分型实际数据。结果Permutation-PCA方法对7种不同遗传模型假设下的模拟病例对照基因分型数据的检验结果差异均具有统计学意义(P均<0.05)。Permutation-PCA方法对于"首吸飘感"基因分型实际数据的检验结果差异具有统计学意义(P<0.05)。结论Permutation-PCA方法对不同遗传模型假设不敏感,适用于复杂疾病关联分析中病例对照基因分型数据的研究。更多还原

【Abstract】 Objective To propose a testing method based on Permutation test and Principle Component Analysis-Permutation-PCA to explore the performance of Permutation-PCA on genotyped data sets under different genetic model assumptions in a case-control study.Methods Based on the control data of the ’heroin-induced positive responses on the first use’ genotyped data,simulated casecontrol genotyped data sets under seven different genetic model assumptions were generated.The Permutation-PCA method was used for the simulated data,and also for the real genotyped data of the ’heroin-induced positive responses on the first use′.Results The results of the Permutation-PCA method under seven different genetic model assumptions were all significant(P-value<0.05),and the result of the Permutation-PCA method on the real genotyped data set was significant(P-value<0.05).Conclusion The Permutation-PCA method is not sensitive to different genetic model assumptions.It is applicable to case-control genotyped data in a complex disease gene mapping study.更多还原